Individual Multimodal Pathway Statistics for Predicting Treatment Response in Late-life Depression

用于预测晚年抑郁症治疗反应的个体多模式通路统计

• 批准号: 10722921
• 负责人: Andrew Robert Gerlach
• 金额: $ 17.58万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-03 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722921
• 关键词: Affect; Age; Algorithms; Antidepressive Agents; Anxiety Disorders; Biological Markers; Brain; Clinical; Cognitive; Data; Data Collection; Depressed mood; Development; Diffusion Magnetic Resonance Imaging; Disease remission; Doctor of Philosophy; Elderly; Engineering; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Health; Image; Impairment; Individual; K-Series Research Career Programs; Knowledge; Magnetic Resonance Imaging; Measurement; Measures; Mediating; Mediator; Mental Depression; Methods; Modality; Modeling; Montgomery and Asberg depression rating scale; Mood Disorders; National Institute of Mental Health; Nature; Neurobiology; Nuclear; Outcome; Participant; Pathway interactions; Performance; Physics; Play; Positioning Attribute; Prediction of Response to Therapy; Prognosis; Psychiatry; Radiology Specialty; Reproducibility; Research; Research Design; Research Personnel; Resolution; Rest; Risk; Role; Scanning; Scheme; Science; Scientist; Severities; Specificity; Structure; Testing; Training; Treatment Protocols; Treatment outcome; Universities; career; clinical predictors; clinical translation; collaborative environment; computational neuroscience; computer framework; depressive symptoms; executive function; geriatric depression; gray matter; high dimensionality; human subject; image processing; image registration; improved; interest; late-life anxiety; machine learning model; multimodal data; multimodal neuroimaging; multimodality; neural circuit; neurobiological mechanism; overtreatment; predictive modeling; primary outcome; recruit; response; statistics; tool; treatment response; white matter; white matter change

Modest response rates to first-line antidepressant treatment for late-life depression (LLD) expose individuals to prolonged depressive symptoms that worsen their prognosis and associated health risks. Biomarkers of treatment response can alleviate this burden by identifying individuals most likely to benefit from antidepressant treatment. MRI measures of brain structure and function are a promising tool to identify such biomarkers, though the performance required for clinical translation has remained elusive. The goal of this proposal is to integrate complementary network measures from structural and functional MRI with clinical measures to generate biologically relevant features that can improve prediction of treatment outcome in LLD. The anticipated impact of this research will provide improved personalization of LLD treatment (NIMH Strategic Objective 3.2), while elucidating the neural circuitry indicative of treatment outcome (Objective 1.3). To achieve this goal, structural, resting state, and diffusion-weighted MRI will be collected from 75 participants with LLD before commencing an algorithmic antidepressant treatment protocol. The role of resting state functional connectivity as a mediator of the relationship between structural connectivity and clinical measures (baseline depression severity and change in depression severity over treatment) will be investigated within key neural circuitry at the group level. Individual Multimodal Pathway Statistics (IMPathS) will be derived to quantify the personalized importance of functional connectivity to the relationship between structural connectivity and depression severity for prediction of treatment outcome at the individual level. Utility of IMPathS will be assessed by their ability to improve performance beyond unimodal MRI and clinical predictors. Dr. Gerlach has a PhD in nuclear engineering and radiological sciences and is completing a transition from computational physics to computational neuroscience. He will require additional training in 1) the neurobiology, clinical manifestations, and treatment of LLD, 2) diffusion-weighted imaging processing and analysis, 3) advanced statistical training for development and testing of IMPathS, 4) human subjects, study design, and data collection. Completion of the training and research plan in this career development award will enable Dr. Gerlach to progress to an independent investigator focused on investigating the neurobiology of late life anxiety and mood disorders through improved integration of multimodal neuroimaging measures. Dr. Gerlach will execute this training and research with the full support of the Department of Psychiatry at the University of Pittsburgh, which is a highly collaborative environment focused on the development of early career scientists.

老年抑郁症(LLD)一线抗抑郁药物治疗的适度应答率使个人暴露于 长期的抑郁症状，恶化他们的预后和相关的健康风险。生物标记物 治疗反应可以通过确定最有可能受益的个人来减轻这一负担 抗抑郁治疗。脑部结构和功能的核磁共振测量是一种很有希望的工具 生物标志物，尽管临床翻译所需的性能仍然难以捉摸。这样做的目的是 建议将结构和功能磁共振成像的补充网络措施与 临床措施，以产生生物学上相关的特征，可以改善治疗预测 在LLD中的结果。这项研究的预期影响将改善LLD的个性化 治疗(NIMH战略目标3.2)，同时阐明指示治疗结果的神经回路 (目标1.3)。为了实现这一目标，将从以下位置收集结构、静息状态和扩散加权MRI 75名患有LLD的参与者在开始算法抗抑郁治疗方案之前。的作用 作为结构连通性和结构连通性之间关系的中介的静止态功能连通性 临床措施(基线抑郁严重程度和治疗期间抑郁严重程度的变化)将是 在团体层面的关键神经回路内进行调查。个体多模式路径统计(IMPath S) 将推导出功能连接对关系的个性化重要性的量化 在个体水平预测治疗结果的结构连通性和抑郁严重程度。实用程序 将根据他们在单模MRI和临床之外改进性能的能力来评估IMPath 预测者。Gerlach博士拥有核工程和放射科学博士学位，正在完成一项 从计算物理到计算神经科学的过渡。他将需要在1)中接受额外培训 LLD的神经生物学、临床表现和治疗，2)弥散加权成像处理和 分析，3)开发和测试IMPath的高级统计培训，4)人类受试者，研究 设计和数据收集。完成该职业发展奖中的培训和研究计划将 使Gerlach博士成为一名独立的研究员，专注于最近的神经生物学研究 通过改进多模式神经成像措施的整合，改善生活焦虑和情绪障碍。Dr。 Gerlach将在精神病学系的全力支持下进行这项培训和研究 匹兹堡大学，这是一个高度协作的环境，专注于早期开发 职业科学家。